Discover the industrial AI insights hidden in your historian

The untapped potential of historians

Industrial historians are well-regarded for their ability to capture high volumes of time series data. Most organizations use historians primarily for alerts and trendline analysis and struggle to access this data for AI-powered insights. Historical data has the potential to offer unparalleled insights when this data becomes available for predictive AI models and generative AI copilots.  Additionally, when historian data can be contextualized and unified with other sources such as work orders, IoT sensor data, images, documents, and drawings, organizations can apply industrial AI to increase asset performance, optimize processes, and augment connected workers.  

AI use cases incorporating historian data

Predictive Maintenance: One of the most impactful applications is AI predictive maintenance, which combines historical data, real-time inputs, failure modes and effects analysis, and predictive AI models to proactively identify equipment failures. By identifying issues before they impact operations, organizations can plan maintenance, reduce unplanned downtime, increase throughput, and extend asset lifespans. This proactive maintenance strategy minimizes operational disruptions and reduces repair costs, offering a significant return on investment. See how Nippon Gases is using predictive maintenance to increase reliability in their facilities. 

Process Optimization: Through process optimization, organizations gain performance insights to improve efficiency and productivity. This involves analyzing historical performance data alongside real-time inputs to quickly identify bottlenecks, adjust process parameters, and optimize resource allocation. For example, the deployment of a process optimization model for set points in a glass manufacturing plant allowed the facility to optimize energy consumption while maintaining quality standard, leading to a significant reduction in energy consumption and a boost in production yield. 

The journey from connectivity to industrial AI

Many manufacturing, oil and gas, and energy organizations have achieved targeted success, often limited to specific use cases or lighthouse sites. To capture the value of AI insights at scale, organizations must use a composable approach to normalize, transform, and unify historian data with other sources. Unifying industrial data into an industrial knowledge graph creates a dynamic, interconnected, and scalable foundation. Predictive and generative AI use cases are then deployed using the data and established relationships in the knowledge graph. Regardless of where your organization is on the industrial AI journey, the following processes outline how SymphonyAI provides the core capabilities industrial organizations need to apply industrial AI to their business objectives.

Connectivity and transformations:

The first step in extracting AI-powered insights from historical data is to establish robust connectivity across all data sources. The SymphonyAI IRIS Foundry industrial dataops platform connects, transforms, unifies, and manages diverse industrial data sources. This integration combines data from all layers of the industrial ecosystem, including historians, ERP, CMMS, LIMS, and other similar sources. With this integration, organizations can perform real-time analysis and historical comparisons, laying the groundwork for establishing a unified name space (UNS). The transformation of raw data into a normalized format understandable by AI models is a pivotal first step, involving standardized universal connectors that bridge operational technology (OT) and information technology (IT).

Creating a dynamic asset hierarchy

Accessing data from various sources addresses one challenge, but unless users understand the relationships between these data points, accessing data provides minimal value. Once data is normalized and flowing, AI-powered contextualization must establish relationships between data sources to create a dynamic asset hierarchy. To create the hierarchy, organizations can reuse one already established in their historian or ERP systems and modify it as needed. Additionally, P&ID ingestion capabilities can build the hierarchy by interpreting relationships in these drawings. By constructing a comprehensive asset hierarchy, organizations can map IT and OT data from enterprise resource planning (ERP), computerized maintenance management systems (CMMS), and IoT devices into a unified structure. The hierarchy serves as the backbone of the knowledge graph, enabling operators to see all relevant data sources in a single location.

Populating an industrial knowledge graph

With the hierarchy established, populating the knowledge graph happens automatically. AI-powered capabilities interpret the relationships between the assets in the hierarchy and reflect the realities of industrial processes. Additional context can be added to the knowledge graph such as energy transfer, mass transfer, and similarity for analysis that extends beyond hierarchical relationships. Taking this one step further, SymphonyAI’s knowledge graph provides performance KPI’s that link critical metrics to their corresponding assets, creating an unparalleled analysis of how assets are impacting processes and vice versa. The industrial knowledge graph becomes a living model of the industrial operation, continuously updated and refined to reflect changes in operations.

Deploying predictive AI models for insight

With a well-populated knowledge graph, organizations can deploy predictive industrial AI models to extract actionable insights. Tools like failure modes and effects analysis (FMEA) and asset templates are vital in this stage, offering structured methodologies for identifying potential failure points and optimizing asset management strategies. Users can train, deploy, and tailor industrial-specific predictive AI models with SymphonyAI’s ML Studio. Use SymphonyAI’s model library or bring your own models, leveraging historical and real-time data to predict equipment failures, optimize maintenance schedules, and increase overall operational efficiency.

Engage with an AI copilot for analysis

Role-based, AI copilots act as intelligent intermediaries between complex data systems and human operators. They provide a natural language interface for engaging with deep insights generated by predictive models and the knowledge graph. With AI copilots, operators can quickly understand alerts, navigate asset hierarchies, forecast future performance, and make data-driven decisions. AI copilots provide transparent insights and put critical information at the operators’ fingertips to drive process improvements and accelerate decision-making.

Transforming historian data into actionable, scalable industrial AI insights is an untapped opportunity in industry today. To capitalize on this opportunity, organizations must establish robust data connectivity, create a dynamic and comprehensive asset hierarchy, and utilizing predictive AI models within a knowledge graph. Following these steps, SymphonyAI has delivered significant improvements in asset performance and process optimization for manufacturing, oil & gas, and energy organizations.